Monday, June 26, 2017

Data Science and Machine Learning Without Mathematics

There is a set of techniques covering all aspects of machine learning (the statistical engine behind data science) that does not use any mathematics or statistical theory beyond high school level. So when you hear that some serious mathematical knowledge is required to become a data scientist, this should be taken with a grain of salt.
The reason maths is a thought to be a requirement is because of the following reasons:
  • Standard tools such as logistic regression, decision trees or confidence intervals, are math-heavy
  • Most employers use standard tools
  • As a result, hiring managers are looking for candidates with a strong math background, mostly for historical reasons
  • Academic training for data scientists are math-heavy for historical reasons (using the professors that used to teach stat classes)
Because of this, you need to really be math savvy to get a "standard" job, so sticking to standard math-heavy training and standard tools work for people interested in becoming a data scientist. To make things more complicated, most of the courses advertised as "math-free" or "learn data science in three days" are selling you snake oil (it won't help you get a job, and many times the training material is laughable.) You can learn data science very quickly, even on your own if you are a self-learner with a strong background working with data and programming (maybe you have a physics background) but that is another story.
Yet there is a set of techniques, designed by a data scientist with a strong mathematical background and long list of publications in top statistical journals that does not use mathematics nor statistical modeling. These techniques work just as well and some of them have been proved to be equivalent to their math-heavy cousins, with the additional bonus of generally being more robust. They are easy to understand and lead to easy interpretations, yet it is not snake oil: it is actually based on years of experience processing large volumes of diverse data, mostly in automated mode.
If you create your own startup, develop your own data science consultancy, or work for an organization that does not care about the tools that you use -- as long as they are cheap, easy to implement, and reliable -- you might consider using these simple, scalable, math-free methods. For instance, if you develop algorithms for stock trading, you wouldn't want to use the same tools as your competitors. These math-free techniques can give you a competitive advantage.
Below, I describe several math-free techniques covering a good chunk of data science, and how they differ from their traditional math-heavy cousins. I use them pretty much every day, though most of the time, in some automated ways.

14 comments:

  1. Thanks for one marvelous posting! I enjoyed reading it; you are a great author. I will make sure to bookmark your blog and may come back someday. I want to encourage that you continue your great posts, have a nice weekend!

    data science training in chennai

    ReplyDelete
  2. This comment has been removed by the author.

    ReplyDelete
  3. Thanks for one marvelous posting! I enjoyed reading it; you are a great author. I will make sure to bookmark your blog and may come back someday. I want to encourage that you continue your great posts, have a nice weekend!

    Data Science Training in Bangalore

    ReplyDelete
  4. This is really an amazing choice for me to solve my data related problems and converting them into the visual forms and presentation, by hiring the best data scientist I can easily solve my data management problems, but I am little bit confuse for hiring them because I have one doubt that they will work or not then my says me that you could check here www.activewizards.com for getting the best data scientist.

    ReplyDelete
  5. Thanks a lot very much for the high quality and results-oriented help. I won’t think twice to endorse your blog post to anybody who wants and needs support about this area. data science training in bangalore.

    ReplyDelete
  6. Have you ever tried learning mathematics by heart or memorizing a large amount of mathematical information? Though the course of action is tough-going, the outcome may be good and even fabulous mootools.net

    ReplyDelete
  7. Really useful information. we are providing best data science online training from industry experts.

    ReplyDelete
  8. Those guidelines additionally worked to become a good way to recognize that other people online have the identical fervor like mine to grasp great deal more around this condition.
    datascience training in chennai

    ReplyDelete
  9. Those guidelines additionally worked to become a good way to recognize that other people online have the identical fervor like mine to grasp great deal more around this condition.


    Data Science Training in Bangalore

    ReplyDelete
  10. I believe there are many more pleasurable opportunities ahead for individuals that looked at your site.

    Data Science Training in Bangalore

    ReplyDelete
  11. Well Said, you have provided the right info that will be beneficial to somebody at all time. Thanks for sharing your valuable Ideas to our vision... Data Science Training in Chennai

    ReplyDelete

Selection of Great Data Science Articles still Worth Reading

These articles are between 3 and 5 year old, but are still valuable today. The methodology used in these articles is modern, and still stat...